论文标题
灰色级别共发生矩阵的深融合肺结节分类
Deep fusion of gray level co-occurrence matrices for lung nodule classification
论文作者
论文摘要
肺癌是对人类健康的严重威胁，由于癌症的迟到，数以百万计的人死亡。因此，至关重要的是尽早发现该疾病。扫描的计算机胸部分析断层扫描被认为是检测和分类肺结节的有效解决方案之一。高精度分析C.T.的必要性肺的扫描图像被认为是检测和分类肺癌的关键挑战之一。引入了新的基于长长的长期内存（LSTM）的深层融合结构，其中，通过新的体积灰度级别的质量级别 - 循环 - 循环 - 循环量表（GLCM）计算，将纹理特征应用于：将结节分类为：良性，恶性和含糊。提出了一种改进的OTSU分割方法与水撇水优化算法（WSA）相结合，以检测肺结节。 OTSU-WSA阈值可以克服先前阈值方法中存在的限制。通过考虑基于2D-GLCM计算的2D-SLICES融合，运行扩展实验以评估此融合结构，并使用具有体积的2.5D-GLCM计算的LSTM融合结构的3D-GLCM进行近似。通过LIDC-IDRI数据集对所提出的方法进行了训练和评估，其中分别获得了94.4％，91.6％和95.8％的精度，敏感性和特异性，用于2D-GLCM融合和97.33％，96％，96％和98％和98％，准确性，敏感性和特定性，均为2.5D。 3D-GLCM融合的收率为98.7％，98％和99％。获得的结果和分析表明，WSA-OTSU方法需要更少的执行时间，并产生更准确的阈值过程。发现基于3D-GLCM的LSTM优于其对应物。
Lung cancer is a severe menace to human health, due to which millions of people die because of late diagnoses of cancer; thus, it is vital to detect the disease as early as possible. The Computerized chest analysis Tomography of scan is assumed to be one of the efficient solutions for detecting and classifying lung nodules. The necessity of high accuracy of analyzing C.T. scan images of the lung is considered as one of the crucial challenges in detecting and classifying lung cancer. A new long-short-term-memory (LSTM) based deep fusion structure, is introduced, where, the texture features computed from lung nodules through new volumetric grey-level-co-occurrence-matrices (GLCM) computations are applied to classify the nodules into: benign, malignant and ambiguous. An improved Otsu segmentation method combined with the water strider optimization algorithm (WSA) is proposed to detect the lung nodules. Otsu-WSA thresholding can overcome the restrictions present in previous thresholding methods. Extended experiments are run to assess this fusion structure by considering 2D-GLCM computations based 2D-slices fusion, and an approximation of this 3D-GLCM with volumetric 2.5D-GLCM computations-based LSTM fusion structure. The proposed methods are trained and assessed through the LIDC-IDRI dataset, where 94.4%, 91.6%, and 95.8% Accuracy, sensitivity, and specificity are obtained, respectively for 2D-GLCM fusion and 97.33%, 96%, and 98%, accuracy, sensitivity, and specificity, respectively, for 2.5D-GLCM fusion. The yield of the same are 98.7%, 98%, and 99%, for the 3D-GLCM fusion. The obtained results and analysis indicate that the WSA-Otsu method requires less execution time and yields a more accurate thresholding process. It is found that 3D-GLCM based LSTM outperforms its counterparts.
